agentic-data-stack-community/examples/simple-ecommerce-analytics/README.md

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Simple E-commerce Analytics - Community Edition

## 🌟 Basic Customer Segmentation with Open Source Tools

This example demonstrates the core capabilities of the AI Agentic Data Stack Framework Community Edition through a complete e-commerce analytics implementation.

### What's Included

- **4 Core AI Agents**: Data Engineer, Data Analyst, Data Product Manager, Data Quality Engineer
- **Basic RFM Analysis**: Recency, Frequency, Monetary customer segmentation
- **3-Dimensional Quality**: Completeness, Accuracy, Consistency validation
- **Real Implementation**: Production-ready SQL and Python code

### Quick Start

```bash
# Install community edition
npm install -g agentic-data-stack-community

# Navigate to example
cd examples/simple-ecommerce-analytics

# Generate sample data
python sample-data/generate-sample-data.py

# Run data exploration
# Use your preferred SQL client to run implementation/data-exploration.sql

# Perform customer segmentation
# Run implementation/customer-segmentation.sql
```

### Project Structure

```
simple-ecommerce-analytics/
├── README.md                           # This file
├── implementation/
│   ├── data-exploration.sql            # Basic data discovery
│   ├── customer-segmentation.sql       # RFM analysis
│   └── basic-quality-validation.py     # 3-dimensional quality checks
├── project-setup/
│   ├── business-requirements.md        # Simplified requirements
│   └── data-contracts/
│       └── customer-data-contract.yaml # Basic data contract
└── sample-data/
    └── generate-sample-data.py         # Sample data generator
```

### Learning Objectives

- **Data Engineering**: Basic pipeline development patterns
- **Data Analysis**: Customer segmentation with RFM analysis
- **Data Quality**: Essential validation and monitoring
- **Project Management**: Requirements gathering and planning

### Key Features Demonstrated

#### 🔧 Data Engineering (Data Engineer Agent)
- ETL pipeline patterns
- Data ingestion workflows
- Basic monitoring setup

#### 📊 Data Analysis (Data Analyst Agent)
- Customer segmentation analysis
- RFM (Recency, Frequency, Monetary) analysis
- Basic reporting and visualization

#### 🎯 Project Management (Data Product Manager Agent)
- Requirements gathering
- Stakeholder coordination
- Value mapping

#### ✅ Data Quality (Data Quality Engineer Agent)
- **Completeness**: Data availability validation
- **Accuracy**: Format and type checking
- **Consistency**: Cross-reference validation

### Business Context

**Scenario**: "Trendy Fashion" online retailer wants to understand customer behavior for targeted marketing campaigns.

**Goals**:
- Segment customers based on purchasing behavior
- Identify high-value customers for retention programs
- Improve marketing campaign effectiveness

**Success Metrics**:
- Customer segments clearly defined
- Marketing team can target campaigns effectively
- Data quality maintained above 85%

### Implementation Guide

#### Step 1: Data Exploration
```sql
-- Run data-exploration.sql to understand the dataset
-- Analyze customer demographics, order patterns, and data quality
```

#### Step 2: Customer Segmentation
```sql
-- Run customer-segmentation.sql for RFM analysis
-- Creates segments: Champions, Loyal Customers, At Risk, etc.
```

#### Step 3: Quality Validation
```python
# Run basic-quality-validation.py
python implementation/basic-quality-validation.py
```

### Expected Results

- **Customer Segments**: 5-7 distinct customer groups
- **Data Quality**: >85% completeness, accuracy, consistency
- **Business Value**: Clear targeting criteria for marketing

### Next Steps

#### For Learning
- Experiment with different segmentation thresholds
- Add additional customer attributes
- Create simple visualizations

#### For Production Use
- Connect to real data sources
- Implement automated data pipelines
- Add monitoring and alerting

#### Upgrade to Enterprise
For advanced features including:
- ML-enhanced segmentation with bias detection
- Real-time collaboration and approval workflows
- Advanced compliance and governance automation
- Healthcare, banking, and enterprise examples

📞 **Contact**: enterprise@agenticdsf.com

### Community Resources

- **GitHub Discussions**: Ask questions and share insights
- **Documentation**: Complete framework documentation
- **Examples**: Additional community examples and tutorials

### Contributing

We welcome community contributions! See our [Contributing Guide](../../CONTRIBUTING.md) for details on:
- Adding new features
- Improving documentation
- Sharing examples
- Reporting issues

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**Framework**: AI Agentic Data Stack Framework - Community Edition  
**License**: MIT  
**Support**: Community-driven via GitHub